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in the current years, speech recognition has emerged as an important research area. To carry out further research on automatic speech recognition, a comprehensive review of existing work in this domain stands useful and constructive for the researchers. This paper has presented a recent literature review on speech recognition considering various existing speech corpora, speech features and different models or classifiers used in speech recognition. Different speech databases have been compared in terms of the number of speakers, type of speakers such as native or acted, age and gender of speakers and speech recording environment. Various techniques for speech signal acquisition and pre-processing of the speech signals are also addressed in this work.

Automatic speech recognition, boundary detection, Feature-extraction, classifier, Mel-Frequency coefficient, phonemes, Speech Filter

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D. Dutta, R.D.Choudhury, S.Gogoi, "Speech Corpora, Feature Extraction Techniques and Classifiers with Special Reference to Automatic Speech Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.372-378, 2019.

The growth of a country depends on the transportation services for traveling safely. Many distresses of asphalt pavements are responsible for the unsafe pavement surface. Potholes are the main reason for the distress of pavement. For detecting and repairing asphalt pavement having potholes, many methods are proposed in the literature for detecting potholes. There are many reasons for the distress of pavement surfaces like heavyweights of vehicles, an unspecified amount of materials, and environmental changes. In this paper, different methods are surveyed like vibration-based method, 3D Reconstruction, and vision-based method for detecting potholes. The vibration-based methods use an accelerometer, 3D laser method uses laser sensor, and vision-based method uses different image processing techniques for detecting potholes. 3D Reconstruction includes 3D laser method, Stereo Vision method, and Kinect Sensor method. The Vision-based method includes a 2D image-based method and Video-based method. In this review paper, the advantages and disadvantages of various methods are also discussed.

Potholes, Vibration-based method, Vision-based method, Stereo vision method, Image processing

[1] A. Akagic, E. Buza, S. Omanovic “Pothole Detection: An Efficient Vision Based Method Using RGB Color Space Image Segmentation”, In the proceeding of the 2017 IEEE conference on MIPRO, pp. 1104-1109, 2017.
[2] K. D. Zoysa, C. Keppitiyagama, G. P. Seneviratne, and W.W.A.T. Shihan, “A public transport system based sensor network for road surface condition monitoring”, In the proceedings of workshop on Networked Systems for Developing Regions, pp. 1-6,2007.
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Jetashri R. Gandhi, U. K. Jaliya, D. G. Thakore, "A Review Paper on Pothole Detection Methods," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.379-383, 2019.

Today many of the institutions use data mining techniques, especially in the field of education. The main purpose of Educational Data Mining (EDM) is to increase the quality of education. Use of data mining methods in educational scenarios will help us to learn student behaviour, their performance, enhance the present student models and efficiently design the course curriculum. Teachers will get an overview into the academic performance and administrators can make policies, execute programmes, and adapt the policies and programmes to enhance the teaching–learning process. Using EDM we can improve student’s achievements and success more efficiently and effectively. Machine Learning methods are very efficient for predicting student performance. The student data depends on the various educational environments. Selection of the correct dataset plays a vital role in these predictions. EDM uses computational approaches to analyze educational problems and data. By applying data mining techniques we can extract valuable information from huge amounts of data. For extracting knowledge from huge volume of data we require sophisticated set of algorithms and data pre-processing techniques. This paper surveys the most relevant studies carried out in the field of student performance enhancement. It also discusses EDM, areas of student performance enhancements and enhancement methods based on classification.

Educational Data Mining, Classification, Knowledge Discovery, Machine Learning, Prediction

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[3] Romero, Cristobal, Sebastian Ventura, “Data mining in education”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3.1, 12–27, 2013.
[4] Leena Khanna, Dr. Shailendra Narayan Singh, Dr. Mansaf Alam, “Educational Data Mining and its Role in Determining Factors Affecting Students Academic Performance: A Systematic Review”, Information Processing (IICIP), I st India International Conference On IEEE, 2016.
[5] Alom, B.M., Matthew Courtney, “Educational Data Mining: Case Study Perspectives from Primary to University Education in Australia”, 2018.
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[8] Kaur, Amanpreet, Amanpreet kaur Batth, “Predicting Instructors Performance using Data Mining Techniques”, International Journal of Engineering Science 12835, 2017.
[9] Jacob John, et al. “Educational Data Mining techniques and their Application”, Green Computing and Internet of Things (ICGCIoT), International Conference on IEEE, 2015.
[10] Han, J., Kamber, M., Pei, J., “Data Mining: Concepts And Techniques”, 3rd edn. Elsevier, Oxford, 2011.
[11] Shahiri, Amirah Mohamed, and Wahidah Husain, “A review on Predicting Students Performance using data mining techniques”, Procedia Computer Science 72: 414- 422, 2015.
[12] Walte, Dipesh, et al. “Overview of algorithm in Educational Data Mining for Higher Education: An Application Perspective”, International Journal of Engineering Research & Technology 3.2, 2014.
[13] Kawchale, Neha, “Prediction of Student Performance in Higher Education System using R Programming”, 2017.
[14] R. Hecht-Nielsen, “Theory of the back propagation neural Network”, In Proceedings of the International Joint Conference on Neural Networks (IJCNN), volume 1, pp. 593–605. IEEE, Washington, DC, 1989.
[15] V.N. Vapnik, “Statistical Learning Theory”, John Wiley & Sons, New York, 1998.
[16]Abazeed, Ashraf, and Moaiad Khder, “A Classification and Prediction Model for Student’s Performance in University Level”, 2017.
[17] Zollanvari, A., Kizilirmak, R.C., Kho, Y.H. and Hernández - Torrano, D., “Predicting students’ GPA and developing intervention Strategies based on self-regulatory learning behaviors”, IEEE Access, 5, pp.23792-23802, 2017.
[18] Nieto, Y., García-Díaz, V., Montenegro, C. and Crespo, R.G., “Supporting academic decision making at higher educational institutions using machine learning-based algorithms”, Soft Computing, pp.1-9, 2018.
[19] JieXu, Kyeong Ho Moon, Mihaela Van Der Schaar, “A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs”, IEEE Journal of Selected Topics in Signal Processing 11.5: 742-753, 2017.
[20] Altujjar, Yasmeen, et al. “Predicting Critical Courses Affecting Students Performance: A Case Study”, Procedia Computer Science 82: 657, 2016.
[21] Mueen, Ahmed, Bassam Zafar, and Umar Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques, International Journal of Modern Education and Computer Science 8.11: 36, 2016.
[22]Guruler, Huseyin, Ayhan Istanbullu, “Modeling Student Performance in Higher Education using Data Mining”, Educational Data Mining. Springer, Cham, 105-124, 2014.
[23]Bhardwaj, Brijesh Kumar, Saurabh Pal, “Data Mining: A Prediction for Performance Improvement using Classification”, ArXiv Preprint arXiv: 1201.3418, 2012.
[24] Asif, R., Merceron, A., Ali, S.A., Haider, N.G, “Analyzing Undergraduate students` performance using educational data Mining”, Computers & Education, 113, pp.177-194, 2017.

Simmi John, Anuj Mohamed, "A Review on EDM Techniques with Special Focus on Student Performance Enhancement," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.384-388, 2019.

The software engineering is the technology which is used to analyze software behavior SDP includes software metrics, their attributes like line of code etc. The main goal of software defects prediction model includes ordering new software modules based on their defect-proneness and classifying them whether it is new software or not. The main purpose of SDP for the ranking is to predict which modules have the most defects to define software quality enhancement. The goal of SDP for the ranking task is to predict the relative defect number, although estimating the precise number of defects of the modules is better than estimating the ranks of modules, because the precise number of defects can give more information than the ranks. The software defect prediction technique is applied in the previous work based on the technique of ANN. In this research work the technique of KNN is applied for the software defect prediction. It is analyzed that proposed technique has high accuracy and less execution time as compared to existing ANN technique.

Fault Prediction, KNN, Software Defect Prediction, NFR ,ANN

[1] Dang Viet Dezung, Atsushi Ohnishi Improvement of Quality of Software Requirements with Requirements Ontology”, International Conference on Quality Software, Jeju, pp 284- 289, 2009
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[7] Senay Tuna Demirel, Resul Das, “Software Requirement Analysis: Research Challenges and Technical Approaches”, IEEE, 2018
[8] Syed Waqas Ali, Qazi Arbab Ahmed, Imran Shafi, “Process to Enhance the Quality of Software Requirement Specification Document”, IEEE, 2018
[9] Ra´ul Navarro-Almanza, Reyes Ju´arez-Ram´ırez, Guillermo Licea, “Towards supporting Software Engineering using Deep Learning: A case of Software Requirements Classification”, 2017 5th International Conference in Software Engineering Research and Innovation
[10] Jorge Rafael Aguilar Cisneros, Genaro de la Rosa Garcia, Carlos Alberto Fernández-y-Fernández, “Software Requirement Specification for the Automotive Sector: The case of a Post-Collision Event Control System”, 2017 5th International Conference in Software Engineering Research and Innovation
[11] Stallin E. F. Silva, Mario L. Cˆortes, “Use of non-functional requirements in software effort estimation: systematic review and experimental results”, 2017 5th International Conference in Software Engineering Research and Innovation
[12] Navid Asadizanjani, Mark Tehranipoor, and Domenic Forte, “PCB Reverse Engineering Using Nondestructive X-ray Tomography and Advanced Image Processing”, 2017, IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY
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[14] Xiaoxing Yang, Ke Tang, Senior Member, IEEE, and Xin Yao, “A Learning-to-Rank Approach to Software Defect Prediction”, IEEE TRANSACTIONS ON RELIABILITY, VOL. 64, NO. 1, MARCH 2015
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[21] K. Dejaeger, T. Verbraken, B. Baesens, “Prediction Models Using Bayesian Network Classifiers,” 39, 2013.
[22] A. Okutan, O.T. Yıldız, “Software defect prediction using Bayesian networks,” Empirical Softw. Eng., 2012.
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Ritika, Er. Saurabh Sharma, "Application of KNN Classification Technique in Detection of Software Fault," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.389-393, 2019.

In today’s world, network security is a very crucial issue. Providing security to the network, services is a major concern these days. Therefore, in this paper we study the concept of Honeypots. A Honeypot is a fake system which lures the attackers. The attackers give in to the temptation and launch attacks. Such attacks help the researchers and organization to study the attack patterns and gain vital information about the attackers. A honeypot is only meant to tantalize the intruders, attackers to perform the malicious activity which results in revealing information about attacks. Thereby, honeypots are quite useful in preventing and counter attacking various types of attacks. We can build Honeynet, Honeywalls using the concept of Honeypots. In this paper, we focus on the concept of Honeypot systems and represent the various aspects of honeypots. We also discuss the various types of honeypots along with its advantages and disadvantages. We also focus on the concepts of Honeynet and Honeywalls.

Honeypot, Honeynet, Honeywalls, Intruders

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[3] Liu Dongxia, Zhang Yongbo, “An Intrusion Detection System Based on Honeypot Technology” , In the Proceedings of 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE2012), Hangzhou, pp. 451-454.
[4] Tao, Jing. Immune-based intrusion prevention model [J]. Network and Information, 200907
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[6] M. Sqalli, R. AlShaikh, E. Ahmed “Towards Simulating a Virtual Distributed Honeynet at KFUPM: A Case Study” UKSim Fourth European Modeling Symposium on Computer Modelling and Simulation.pp. 316-321, 2010
[7] Ariel Bar, Bracha Shapira, Lior Rokach and Moshe Unger, “Identifying Attack Propagation Patterns in Honeypots using Markov Chains Modeling and Complex Networks Analysis” IEEE International Conference on Software Science, Technology and Engineering , pp. 28-36, 2016.
[8] Thesis on “Honeypots in Network Security” by Deniz Akkaya- Fabien Thalgott, School of Computer Science, Physics and Mathematics, Linnaeus University, 29th June 2010.
[9] Gérard Wagener. “Self-Adaptive Honeypots Coercing and Assessing Attacker Behaviour” Computer Science [cs]. Institut National Polytechnique de Lorraine - INPL, 2011. English.
[10] Jules Pagna Disso, Kevin Jones, Steven Bailey, “A Plausible Solution SCADA Security: Honeypot Systems”Eighth International Conference on Broadband, Wireless Computing, Communication and Applications,pp. 443-448, 2013.
[11] Mohammed H. Sqalli, Shoieb Arshad, Mohammad Khalaf, Khaled Salah, “Identifying Scanning Activities in Honeynet Data using Data Mining” Third International Conference on Computational Intelligence, Communication Systems and Networks, pp. 178-183, 2011.
[12] A. Mairh, et al., Honeypot in network security: a survey, In: Proceedings of the 2011 International Conference on Communication, Computing & Security. ACM, 2011. p. 600-605.
[13] L. Spitzner, Honeypots: Catching the insider threat, In: Computer Security Applications Conference 2003, Proceedings. 19th Annual. IEEE, 2003. p. 170-179, 2003
[14] Dissertation on “Deception Techniques Using Honeypots” by Amit D. Lakhani, Information Security Group Royal Holloway, University of London, UK.
[15] Keith Harrison, James R. Rutherford, and Gregory B. White “The Honey Community: Use of Combined Organizational Data for Community Protection” 48th Hawaii International Conference on System Sciences, pp. 2288-2297, 2015.

Neha Titarmare, Nayankumar Hargule, Anand Gupta, "An Overview of Honeypot Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.394-397, 2019.

The finite energy of batteries associated with wireless sensor networks is a major constraint which limits its lifetime. One of the methods to overcome this major limitation is energy harvesting systems. Thither are many energy sources available nowadays, but solar energy is flexible, mature and external power source so it is broadly used for energy harvesting in WSN to enhance the life of the network used. This paper presented an overview of solar energy harvesting system and the impact of solar energy harvesting on Wireless Sensor Network. We have also propounded the various energy harvesting sources that are used for WSNs and energy harvesting process. This paper also describes the supercapacitor and various recharging batteries.

Wireless sensor network (WSN), Energy Harvesting (EH), Solar energy harvesting (SEH)

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[8] Arpita Jaitawat, Arun Kumar Singh, “Battery and supercapacitor imperfections modeling and comparison for RF energy harvesting wireless sensor network”, Springer Science+Business Media, LLC, part of Springer Nature, https://doi.org/10.1007/s11276-018-1831-z (2018).
[9] Yin Li and Ronghua Shi, “An intelligent solar energy-harvesting system for wireless sensor networks”, EURASIP Journal on Wireless Communications and Networking, Springer, DOI 10.1186/s13638-015-0414-2, (2015).
[10] Dong Kun Noh, Junbeom Hur, “Using a dynamic backbone for efficient data delivery in solar-powered WSNs”, Journal of Network and Computer Applications 35 (2012) 1277–1284, (2012).
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[15] Ahmad H. Dehwaha, Shahrazed Elmetennania, Christian Claudel,“UD-WCMA: An energy estimation and forecast scheme for solar powered wireless sensor networks”, Journal of Network and Computer Applications, DOI: 10.1016/j.jnca.2017.04.003(20) (2017).
[16] Dong Kun Noh, Kyungtae Kang, “Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance”, Journal of Computer and System Sciences 77 (2011) 917–932 , DOI:10.1016/j.jcss.2010.08.008 , (2011).
[17] Mustapha Khiati, Djamel Djenouri,“ Adaptive learning-enforced broadcast policy for solar energy harvesting wireless sensor networks”, Computer networks, DOI:10.1016/j.comnet.2018.07.016 (2018) .
[18] Gwo-Jiun Horng , Tun-Yu Chang , Sheng-Tzong Cheng, “ An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment”, Expert Systems with Applications41(2014)3143–3156, DOI:10.1016/j.eswa.2013.11.016 0957-4174 , (2013).

Harmandeep Kaur, Avtar Singh Buttar, "A Review on Solar Energy Harvesting Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.398-404, 2019.

Train delay is one of the foremost problems in the railway systems across the world. According to the TOI newspaper, In India there are about 25.3 million people were used to travel by train in 2006 and this drastically increased year by year. In 2018, every day at least 80 million people in India prefer to travel by trains[1]. Categorically in India, most of the trains unable to run on their scheduled time due to poor signaling and less number of railway tracks. This implies that travellers might get delayed to reach their respective destinations. The aim of this paper is to present the prediction of Train delay in Indian Railways through machine learning techniques to achieve higher accuracy. In the proposed model, we used 3 different machine learning methods (Multivariate regression, Neural Network, and Random Forest) which have been compared with different settings to find the most accurate method. To compare different methods, we consider training time and accuracy of the method over the test data set. Trains in India get delayed frequently, and if we can predict this in advance - it would be a great help for the passengers to plan their journey according to their works.

Train delay, Multivariate Regression, Neural Network, Random Forest

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[2] Ministry of Railway. "Indian Railways Statistical Publications 2016-17: PassengerBusiness" p. 23. Archived (PDF) from the original on 3 March 2018. Retrieved: 2 March 2018
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[8] Hansen, I.A. & Goverde, Rob & J. van der Meer, Dirk. “Online train delay recognition and running time prediction.” IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. 1783 - 1788. 10.1109/ITSC.2010.5625081. (2010).
[9] Y. Masoud et al. “ Railway passenger train delay prediction via neural network model” JOURNAL OF ADVANCED TRANSPORTATION, 47:355–368, (2013)
[10] S. Pongnumkul et al. "Improving arrival time prediction of Thailand`s passenger trains using historical travel times," 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 307-312, 2014
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Mohd Arshad, Muqeem Ahmed, "Prediction of Train Delay in Indian Railways through Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.405-411, 2019.

Indoor navigation has become as important as outdoor navigation. Navigating in big, unknown indoor environments with static 2D maps is a challenge, especially when time is a critical factor. Here we try to present a solution to this problem through an augmented reality based indoor navigation application to assist people in navigating indoor environments, which runs on mobile devices like smartphones or tablets. The excellent computing capabilities in today’s high-end phones or smartphones in combination with their multiple sensors, such as Global Positioning System (GPS), motion sensors, camera, wireless receivers, will allow us to develop a system more advanced than any previous attempts for indoor navigation. The solution will utilize an indoor image based positioning system that takes advantage of smartphones augmented reality (AR) and inertial tracking. The system will have the capability of delivering continuous 3D positioning and orientation of the mobile device which makes it ideal for any navigational application.

Augmented Reality (AR), Global Positioning System (GPS), Indoor Navigation, Technology

[1] U. Rehman, and S. Cao, “Augmented Reality-based Indoor Navigation: A Comparative Analysis of Handheld Devices vs. Google Glasses”, In the Proceedings of the 2016 IEEE Conference on Human Machine Systems, India, pp. 47(1), 140–151.
[2] “Feature-based indoor navigation app using Augmented Reality”. Sebastian Kasprzak, Andreas Komninos, Peter Barrie, United Kingdom.
[3] Mehdi Mekni, Andre Lemieux, “Augmented Reality: Applications, Challenges and Future Trends”. In the Proceedings of the Applied Computational Science, University of Minnesota.
[4] “A Review Paper on Augmented Reality”, International Journal of Computer Sciences and Engineering, Volume 6, Issue 5, May 2018.
[5] “Natural markers for augmented reality-based indoor navigation and facility maintenance”. Christian Kotch, Matthias Negas, Markus Konig, 20th August 2014, Germany.
[6] R. Silva, J. C. Oliveira, G. A. Giraldi, “Introduction to Augmented Reality”, In the Proceedings of the National Laboratory of Scientific Computation, Brazil.
[7] “Indoor localization and navigation using smartphones augmented reality and inertial tracking”. Buti Al Delail, Luis Weruaga, M. Jamal Zemerly, UAE.
[8] “HyMoTrack: A Mobile AR Navigation System for Complex Indoor Environments”. Georg Gerstweiler, Emanuel Vonach and Hannes Kaufmann, 24th December, 2015, Austria.
[9] Dree Barclay, Maricar Aliasut, Llandro Ojeda, “Augmented Reality in Location Tracking”, University of Manitoba, Cannada, pp. 4, 2017.
[10] Nehla Ghouaiel , Samir Garbaya , Jean-Marc Cieutat , Jean-Pierre Jessel, “Mobile Augmented Reality in Muesums: Towards Enhancing Visitor’s Learning Experience”, International Journal of Virtual Reality, 2016, pp 21-pp 31.
[11] Sebageniz Jason, Suchithra R, “ Scheduling Reservations of Virtual Machines in Cloud Data Center for Energy Optimization”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.16-26, December (2018).
[12] Mutkule Prasad R., “ Interactive Clothing Based on IoT using QR Code and Mobile Application”, International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-6, December 2018 Research Paper.

Waheeda Dhokley, Asif Syed, Nitika Tomar, Riya Patil, "A Study on Augmented Reality Assisted Navigation App Using Machine Learning and Computer Vision," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.412-415, 2019.

Augmented Reality is an interactive and real-world environment where the objects in the real-world are augmented by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, and somatosensor. Augmented Reality (AR) is a new interface technology which brings 3D model in the real time environment. To manipulate 3D models in real time environment through the Smartphone`s Vuforia technology is used. Visualization of the model can be done by using HMD (Head Mounted Display). Develop a model which will forecast a dynamic 3D model from 2D house plans. The system assembles 3D models and overlays virtual model on the real 2D Blueprint of the house (Architectural/Hand-drawn). Harris corner detection algorithm is used and Detection of Doors/Windows are done through Template matching algorithm. It is mainly based on Image Processing by using OpenCV library functions.

HMD(Head Mounted Display), Computer Vision, OpenCV

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[3] M. Weber, M. Liwicki, and A. Dengel, “a.SCAtch - A Sketch-Based Retrieval for Architectural Floor Plans,” 12th International Conference on Frontiers of Handwriting Recognition., 2010, pp. 289–294.
[4] G. S. Lluis-Pere de las Heras, Joan Mas and E. Valveny, “Wall Patch-Based Segmentation in Architectural Floorplans,”in 11th International Conference on Document Analysis and Recognition., 2011.
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W78 Conference, Istanbul, Turkey, Oct 2009, 8 pp.
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[7] Y. Shen, S. K. Ong, and A. Y. C. Nee, "Collaborative design in 3Dspace," p. 29, Aug. 2008.
[8] G. Carmelo, L. Delfa, and V. Catania, "Accurate indoor navigation usingSmartphone, Bluetooth low energy and visual tags," jun.11,2016.

T.Gowrisankari, M.Rahul Raj, A.Kousika, G. Balachandar, "Interactive 3D Model Construction From House Plans using Augmented Reality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.416-420, 2019.

This study helps organizations and individuals understand how cloud computing can provide them with reliable, customized and cost-effective services in a wide variety of applications. In this paper, we have tried to explore various cloud computing services, applications and characteristics; we give various examples for cloud services delivered by the most common Cloud Service Providers (CSPs) such as Google, Microsoft, and Amazon. We have also discussed cloud computing service models and their benefits.

Cloud Computing, Virtualization, Data recovery, E-Governance, Service provider

[1] Mell P, Grance T, Others. The NIST definition of cloud computing [Internet]. [cited 18 Sep 2017]. National Institute of Standards and Technology; 2011. Report No.: Special Publication 800–145.
[2] Radu Prodan and Simon Ostermann, “A Survey and Taxonomy of Infrastructure as a Service and Web Hosting Cloud Providers”, 10th IEEE/ACM International Conference on Grid Computing, 2009
[3] http://en.wikipedia.org/wiki/Cloud_computing
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[5] K. Chard, S. Caton, O. Rana and K. Bubendorfer, “Social Cloud: Cloud Computing in Social Networks”, 3rd IEEE International Conference on Cloud Computing, Miami, FL, USA, July 5-10,2010.
[6]Cloud Computing vs. Virtualization http://www.learncomputer.com/cloud-computing-vsvirtualization/
[7] Andrew Joint and Edwin Baker, “Knowing the past to understand the present- issues in the contracting for cloud based services”, Computer Law and Security Review 27, pp 407-415, 2011
[8] Vania Goncalves and Pieter Ballon, “Adding value to the network: Mobile operators’ experiments with Software-as-a-Service and Patform-as-a-Service models”, Telematics and Informatics 28, pp 12-21, 2011
[9] NIST, http://www.nist.gov/itl/cloud/index.cfm
[10] GTSI Group, “Cloud Computing - Building a Framework for Successful Transition,” White Paper, GTSI Corporation, 2009.
[11] T. Dillon, C. Wu and E. Chang, “Cloud Computing: Issues and Challenges”, 24th IEEE International Conference on Advanced Information Networking and Applications, 2010.
[12]P. Mell and T. Grance, “The NIST Definition of Cloud Computing” Recommendation of NIST, Special Publication 800-145 2011http://csrc.nist.gov/publications/nistpubs/800-145/SP800-45.pdf
[13]Z. Wang, “Security and Privacy Issues Within Cloud Computing”, IEEE Int. conference on computational and information sciences, Chengdu, China, Oct. 2011.
[14] Ahmed Youssef and Manal Alageel “Security Issues in Cloud Computing”, in the GSTF International Journal on Computing , Vol.1 No. 3, 2011.
[15] Dimitrios Zissis and Dimitrios Lekkas, “Addressing cloud computing security issues”, Future Geberation Computer Systems 28, pp. 583-592, 2012.
[16] Rajnish Choubey, Rajshree Dubey and Joy Bhattacharjee, “ A Survey on Cloud Computing Security, Callenges and Threats”, International Journal on Computer Science and Engineering (IJCSE), vol. 3, No. 3, 2011.

Aaqib Rashid, Amit Chaturvedi, "Cloud Computing Characteristics and Services: A Brief Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.421-426, 2019.